Current statistical techniques are unable to deal with longitudinal data from studies of aging that incorporate large numbers of variables from different scientific domains. We propose to undertake a multidisciplinary effort with a team of gerontological scientists to develop and apply a set of statistical models designed to circumvent some of the deficiencies of conventional models of health and disease as applied to the study of aging. The models we develop will be utilized in an extensive analysis of two longitudinal studies conducted by the Center for The Study of Aging and Human Development of the Duke University Medical Center. The primary model to be used is a fuzzy set-theoretic based one, designed to detect sub-patterns of illness or health-states, called pure types, within a broad class of patients or subjects. An individual's health at a given time is expressed in terms of degrees of involvement, measured by grades of membership, in each of the pure types. Application of this model will define sets of latent variables which we expect will discriminate among three patterns of the effects of aging: Invariant, reversible, and irreversible. Conditions that are reversible are to be distinguished from those that are at best prevented or at worst delayed in onset. We seek to establish conditions for intervention that will delay the onset or reduce the effect of the irreversible trends, such as terminal drop or dementia. Other models to be developed and applied include: (1) a grade of membership model for continuous variables; (2) the method of essential components, for analysis of electrical signals of biological origin, to identify the components, their amplitude and timing, either in repetitions of heartbeats or subjects followed over time; (3) a random walk model of human mortality and aging, which considers the role of certain types of state space and time scales for a representation of individual physiological status change and death, and provides a measure of the average "force" of aging over intervals in a longitudinal study; (4) a Markov network method for analyzing covariance structures, for studying such phenomena as changes in intellectual functioning over time.